Accuracy Enhancement of Mining Association Rules

Provided by: International Journal of Advanced Research in Computer Science & Technology (IJARCST)
Topic: Data Management
Format: PDF
A new anonymization algorithm called Non-homogeneous Generalization with Sensitive Value Distributions (NSGVD) has with make use of data mining algorithm as association rule been devised. This algorithm helps to generate minimum anonymity and diversity parameters along with an information loss measure. In the experiments, using eight datasets and four different classification algorithms, it is shown that classifiers induced from data generalized by NSGVD tend to be more accurate than classifiers induced using state of the art anonymization algorithms.

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